MIS-CH11: Managing Knowledge

6.1 Copyright © 2015 Pearson Education, Inc. publishing as Prentice Hall
Managing KnowledgeManaging Knowledge
Chapter 11
VIDEO CASES
Video Case 1: How IBM’s Watson Became a Jeopardy Champion
Video Case 2: Tour: Alfresco: Open Source Document Management System
Instructional Video 1: Analyzing Big Data: IBM Watson: Watson After Jeopardy
Instructional Video 2: Teamwork and Collaboration: John Chambers on Collaboration vs.
Command and Control in Web 2.0

11.2 Copyright © 2016 Pearson Education Ltd.
Management Information Systems
Chapter 11: Managing Knowledge
• What is the role of knowledge management and
knowledge management programs in business?
• What types of systems are used for enterprise-wide
knowledge management and how do they provide
value for businesses?
• What are the major types of knowledge work
systems and how do they provide value for firms?
• What are the business benefits of using intelligent
techniques for knowledge management?
Learning Objectives

• Problem: Large global organization with fragmented
systems and data
• Solution: New systems to provide enterprise-wide
data for management reporting and analysis
– Oracle Hyperion Enterprise Performance Management and
Business Intelligence
• Demonstrates the need for global firms to have
integrated systems for global reporting
• Illustrates the use of enterprise software to provide
timely and more accessible information
Fiat: Real Time Management with Business Intelligence

• Knowledge management systems among fastest
growing areas of software investment
• Information economy
– 37 percent U.S. labor force: knowledge and information workers
– 45 percent U.S. GDP from knowledge and information sectors
• Substantial part of a firm’s stock market value is
related to intangible assets: knowledge, brands,
reputations, and unique business processes
• Well-executed knowledge-based projects can
produce extraordinary ROI
The Role of Knowledge Management in Business

• Important dimensions of knowledge
– Knowledge is a firm asset.
• Intangible
• Creation of knowledge from data, information, requires
organizational resources
• As it is shared, experiences network effects
– Knowledge has different forms.
• May be explicit (documented) or tacit (residing in
minds)
• Know-how, craft, skill
• How to follow procedure
• Knowing why things happen (causality)

• Important dimensions of knowledge (cont.)
– Knowledge has a location.
• Cognitive event
• Both social and individual
• “Sticky” (hard to move), situated (enmeshed in firm’s
culture), contextual (works only in certain situations)
– Knowledge is situational.
• Conditional: Knowing when to apply procedure
• Contextual: Knowing circumstances to use certain tool

• To transform information into knowledge, firm must expend
additional resources to discover patterns, rules, and
contexts where knowledge works
• Wisdom:
– Collective and individual experience of applying knowledge to solve
problems
– Involves where, when, and how to apply knowledge
• Knowing how to do things effectively and efficiently in ways
others cannot duplicate is prime source of profit and
competitive advantage
– For example, Having a unique build-to-order production system

• Organizational learning
– Process in which organizations learn
•Gain experience through collection of
data, measurement, trial and error, and
feedback
•Adjust behavior to reflect experience
–Create new business processes
–Change patterns of management decision
making

• Knowledge management
– Set of business processes developed in an
organization to create, store, transfer, and apply
knowledge
• Knowledge management value chain:
– Each stage adds value to raw data and information as
they are transformed into usable knowledge
– Knowledge acquisition
– Knowledge storage
– Knowledge dissemination
– Knowledge application

• Knowledge management value chain
1. Knowledge acquisition
•Documenting tacit and explicit knowledge
– Storing documents, reports, presentations, best
practices
– Unstructured documents (e.g., e-mails)
– Developing online expert networks
•Creating knowledge
•Tracking data from TPS and external sources

2. Knowledge storage
•Databases
•Document management systems
•Role of management:
– Support development of planned knowledge storage systems.
– Encourage development of corporate-wide schemas for
indexing documents.
– Reward employees for taking time to update and store
documents properly.

3. Knowledge dissemination
•Portals, wikis
•E-mail, instant messaging
•Search engines
•Collaboration tools
•A deluge of information?
– Training programs, informal networks, and shared
management experience help managers focus
attention on important information.

4. Knowledge application
•To provide return on investment,
organizational knowledge must become
systematic part of management decision
making and become situated in decision-
support systems.
– New business practices
– New products and services
– New markets

Knowledge management today involves both information systems activities and a host of enabling management
and organizational activities.
FIGURE 11-1
The Knowledge Management Value Chain

• Organizational roles and responsibilities
– Chief knowledge officer executives
– Dedicated staff / knowledge managers
– Communities of practice (COPs)
• Informal social networks of professionals and
employees within and outside firm who have similar
work-related activities and interests
• Activities include education, online newsletters, sharing
experiences and techniques
• Facilitate reuse of knowledge, discussion
• Reduce learning curves of new employees

• Three major types of knowledge management
systems:
1. Enterprise-wide knowledge management systems
• General-purpose firm-wide efforts to collect, store, distribute, and
apply digital content and knowledge
1. Knowledge work systems (KWS)
• Specialized systems built for engineers, scientists, other
knowledge workers charged with discovering and creating new
knowledge
1. Intelligent techniques
• Diverse group of techniques such as data mining used for various
goals: discovering knowledge, distilling knowledge, discovering
optimal solutions

There are three major categories of knowledge management systems, and each can be broken down further into
more specialized types of knowledge management systems.
FIGURE 11-2
MAJOR TYPES OF KNOWLEDGE MANAGEMENT SYSTEMS

• Three major types of knowledge in enterprise:
1. Structured documents
• Reports, presentations
• Formal rules
1. Semistructured documents
• E-mails, videos
1. Unstructured, tacit knowledge
• 80 percent of an organization’s business content
is semistructured or unstructured.
Enterprise-Wide Knowledge Management Systems

• Enterprise content management
systems
– Help capture, store, retrieve, distribute, preserve
•Documents, reports, best practices
•Semistructured knowledge (e-mails)
– Bring in external sources
•News feeds, research
– Tools for communication and collaboration
•Blogs, wikis, and so on

An enterprise content management system has
capabilities for classifying, organizing, and managing
structured and semistructured knowledge and making
it available throughout the enterprise.
FIGURE 11-3
AN ENTERPRISE CONTENT MANAGEMENT SYSTEM

• Enterprise content management systems
– Key problem—Developing taxonomy
•Knowledge objects must be tagged with
categories for retrieval
– Digital asset management systems
•Specialized content management systems for
classifying, storing, managing unstructured
digital data
•Photographs, graphics, video, audio

• Locating and sharing expertise
– Provide online directory of corporate experts in
well-defined knowledge domains
– Search tools enable employees to find
appropriate expert in a company
– Social networking and social business tools for
finding knowledge outside the firm
• Saving, tagging, sharing Web pages

• Learning management systems (LMS)
– Provide tools for management, delivery, tracking, and
assessment of employee learning and training
– Support multiple modes of learning
• CD-ROM, Web-based classes, online forums, and so on
– Automates selection and administration of courses
– Assembles and delivers learning content
– Measures learning effectiveness
– Massively open online courses (MOOCs)
– Web course open to large numbers of participants

• Knowledge work systems
– Systems for knowledge workers to help create new
knowledge and integrate that knowledge into business
• Knowledge workers
– Researchers, designers, architects, scientists, engineers
who create knowledge for the organization
– Three key roles:
1. Keeping organization current in knowledge
2. Serving as internal consultants regarding their areas of
expertise
3. Acting as change agents, evaluating, initiating, and
promoting change projects
Knowledge Work Systems

• Requirements of knowledge work systems
– Sufficient computing power for graphics,
complex calculations
– Powerful graphics and analytical tools
– Communications and document management
– Access to external databases
– User-friendly interfaces
– Optimized for tasks to be performed (design
engineering, financial analysis)

Knowledge work systems
require strong links to external
knowledge bases in addition to
specialized hardware and
software.
FIGURE 11-4
REQUIREMENTS OF KNOWLEDGE WORK SYSTEMS

• Examples of knowledge work systems
– CAD (computer-aided design):
• Creation of engineering or architectural designs
• 3D printing
– Virtual reality systems:
• Simulate real-life environments
• 3D medical modeling for surgeons
• Augmented reality (AR) systems
• VRML
– Investment workstations:
• Streamline investment process and consolidate internal, external
data for brokers, traders, portfolio managers

Read the Interactive Session and discuss the following questions
Interactive Session: Technology
• Analyze Firewire using the value chain and competitive forces
models.
• What strategies is Firewire using to differentiate its product,
reach its customers, and persuade them to buy its products?
• What is the role of CAD in Firewire’s business model?
• How did the integration of online custom board design
software (CBD), CAD, and computer numerical control (CNC)
improve Firewire’s operations?
Firewire Surfboards Light Up with CAD

• Intelligent techniques: Used to capture
individual and collective knowledge and to
extend knowledge base
– To capture tacit knowledge: Expert systems, case-based
reasoning, fuzzy logic
– Knowledge discovery: Neural networks and data mining
– Generating solutions to complex problems: Genetic
algorithms
– Automating tasks: Intelligent agents
• Artificial intelligence (AI) technology:
– Computer-based systems that emulate human behavior
Intelligent Techniques

• Expert systems:
– Capture tacit knowledge in very specific and limited
domain of human expertise
– Capture knowledge of skilled employees as set of
rules in software system that can be used by others
in organization
– Typically perform limited tasks that may take a few
minutes or hours, for example:
• Diagnosing malfunctioning machine
• Determining whether to grant credit for loan
– Used for discrete, highly structured decision making

An expert system contains a
number of rules to be followed.
The rules are interconnected;
the number of outcomes is
known in advance and is
limited; there are multiple paths
to the same outcome; and the
system can consider multiple
rules at a single time. The rules
illustrated are for simple credit-
granting expert systems.
FIGURE 11-5
RULES IN AN EXPERT SYSTEM

• How expert systems work
– Knowledge base: Set of hundreds or thousands of
rules
– Inference engine: Strategy used to search knowledge
base
• Forward chaining: Inference engine begins with
information entered by user and searches knowledge
base to arrive at conclusion
• Backward chaining: Begins with hypothesis and asks
user questions until hypothesis is confirmed or
disproved

An inference engine works by searching through the rules and “firing” those rules that are triggered by facts
gathered and entered by the user. Basically, a collection of rules is similar to a series of nested IF statements in
a traditional software program; however, the magnitude of the statements and degree of nesting are much
greater in an expert system.
FIGURE 11-6
INFERENCE ENGINES IN EXPERT SYSTEMS

• Successful expert systems:
– Con-Way Transportation built expert system to automate and
optimize planning of overnight shipment routes for nationwide
freight-trucking business
• Most expert systems deal with problems of
classification.
– Have relatively few alternative outcomes
– Possible outcomes are known in advance
• Many expert systems require large, lengthy, and
expensive development and maintenance efforts.
– Hiring or training more experts may be less expensive

• Case-based reasoning (CBR)
– Descriptions of past experiences of human specialists (cases),
stored in knowledge base
– System searches for cases with characteristics similar to new
one and applies solutions of old case to new case
– Successful and unsuccessful applications are grouped with case
– Stores organizational intelligence: Knowledge base is
continuously expanded and refined by users
– CBR found in
• Medical diagnostic systems
• Customer support

Case-based reasoning
represents knowledge as a
database of past cases and their
solutions. The system uses a
six-step process to generate
solutions to new problems
encountered by the user.
FIGURE 11-7
HOW CASE-BASED REASONING WORKS

• Fuzzy logic systems
– Rule-based technology that represents imprecision used
in linguistic categories (e.g., “cold,” “cool”) that represent
range of values
– Describe a particular phenomenon or process
linguistically and then represent that description in a
small number of flexible rules
– Provides solutions to problems requiring expertise that is
difficult to represent with IF-THEN rules
• Autofocus in cameras
• Detecting possible medical fraud
• Sendai’s subway system acceleration controls

The membership functions for the input called temperature are in the logic of the thermostat to control the room
temperature. Membership functions help translate linguistic expressions such as warm into numbers that the
computer can manipulate.
FIGURE 11-8
FUZZY LOGIC FOR TEMPERATURE CONTROL

• Machine learning
– How computer programs improve performance
without explicit programming
• Recognizing patterns
• Experience
• Prior learnings (database)
– Contemporary examples
• Google searches
• Recommender systems on Amazon, Netflix

• Neural networks
– Find patterns and relationships in massive amounts
of data too complicated for humans to analyze
– “Learn” patterns by searching for relationships,
building models, and correcting over and over again
– Humans “train” network by feeding it data inputs for
which outputs are known, to help neural network
learn solution by example
– Used in medicine, science, and business for problems
in pattern classification, prediction, financial
analysis, and control and optimization

A neural network uses rules it “learns” from patterns in data to construct a hidden layer of logic. The hidden
layer then processes inputs, classifying them based on the experience of the model. In this example, the neural
network has been trained to distinguish between valid and fraudulent credit card purchases
FIGURE 11-9
HOW A NEURAL NETWORK WORKS

Read the Interactive Session and discuss the following questions
Interactive Session: Organizations
• What technologies is New York employing to improve the
quality of life of its citizens?
• What are the people, organization, and technology issues that
should be addressed by “smart city” initiatives?
• What problems are solved by “smart cities?” What are the
drawbacks?
• Give examples of four decisions that would be improved in a
“smart city.”
Big Data Makes Cities Smarter

• Genetic algorithms
– Useful for finding optimal solution for specific problem
by examining very large number of possible solutions for
that problem
– Conceptually based on process of evolution
• Search among solution variables by changing and
reorganizing component parts using processes such as
inheritance, mutation, and selection
– Used in optimization problems (minimization of costs,
efficient scheduling, optimal jet engine design) in which
hundreds or thousands of variables exist
– Able to evaluate many solution alternatives quickly

This example illustrates an initial population of “chromosomes,” each representing a different solution. The
genetic algorithm uses an iterative process to refine the initial solutions so that the better ones, those with the
higher fitness, are more likely to emerge as the best solution.
FIGURE 11-11
THE COMPONENTS OF A GENETIC ALGORITHM

• Intelligent agents
– Work without direct human intervention to carry out
specific, repetitive, and predictable tasks for user,
process, or application
• Deleting junk e-mail
• Finding cheapest airfare
– Use limited built-in or learned knowledge base
– Some are capable of self-adjustment, for example: Siri
– Agent-based modeling applications:
• Systems of autonomous agents
• Model behavior of consumers, stock markets, and
supply chains; used to predict spread of epidemics

Intelligent agents
are helping P&G
shorten the
replenishment
cycles for products
such as a box of
Tide.
FIGURE 11-12
INTELLIGENT AGENTS IN P&G’S SUPPLY CHAIN NETWORK

• Hybrid AI systems
– Genetic algorithms, fuzzy logic, neural
networks, and expert systems integrated
into single application to take advantage
of best features of each
– For example: Matsushita “neurofuzzy”
washing machine that combines fuzzy logic
with neural networks

MIS-CH11: Managing Knowledge

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